What is the Byzantine General Problem?

The meaning of Byzantine General Problem refers to a computer science problem that arises in distributed computing systems. It occurs when multiple generals (or nodes) have to coordinate their actions in order to achieve a common goal. This is complicated by the fact that the generals cannot trust each other, and some of them may be actively trying to undermine the other generals.

The problem was first proposed by Leslie Lamport in the 1980s, and is one of the most important problems in distributed computing because it serves as the basis for many consensus-based protocols. It is based on the historical story of the Battle of Constantinople, where the Byzantine generals had to decide whether to attack or retreat.

In essence, the problem requires the generals to agree on a common course of action. The generals must also reach a consensus despite the presence of malicious actors. To do this, the generals must be able to communicate with each other, but must also be able to identify malicious actors and prevent them from interfering with the consensus process.

The Byzantine General Problem is a challenging problem, and many solutions have been proposed over the years. These solutions typically use a combination of cryptography and communication protocols to ensure the integrity of the consensus process. For instance, one solution is to use a Byzantine Fault Tolerance (BFT) algorithm, which allows the generals to arrive at a consensus even if some of them are malicious.

Overall, the Byzantine General Problem is an important problem in distributed computing, and it serves as the basis for many consensus protocols. By understanding the problem, researchers and developers are able to build robust distributed systems that can resist attacks even in the presence of malicious actors.

Simplified Example

The Byzantine General Problem is an issue of trust and communication in distributed computing systems. It is best explained through an analogy of a group of generals surrounding an enemy city, each with their own battalion of troops. In order to successfully take the city, all generals must agree to attack at the same time. However, due to the potential of enemy spies, some generals may try to deceive the others by sending false messages. The generals must come to a consensus on whether or not they should launch an attack while also avoiding the possibility of being deceived. This problem is the same in a distributed system, where different computers must come to a consensus on data without the risk of manipulation by malicious actors.

Who Invented the Byzantine General Problem?

Leslie Lamport, a pioneering computer scientist, is recognized for his groundbreaking contributions to distributed systems and fault tolerance, notably introducing the Byzantine Generals Problem.

In 1982, Lamport introduced this theoretical conundrum in a seminal paper titled "The Byzantine Generals Problem", demonstrating the challenges of achieving consensus among a network of unreliable or malicious components. The problem was formulated as a metaphorical scenario where a group of Byzantine generals, commanding different divisions of an army, must coordinate their attack or retreat on a common enemy. Lamport's innovative formulation revealed the complexities of ensuring a consensus among the generals, especially when some might be traitorous or unreliable, highlighting the need for fault-tolerant algorithms to reach a reliable agreement despite the presence of deceitful or faulty elements in a distributed system. His work laid the theoretical foundation for consensus protocols in distributed systems, profoundly influencing the development of blockchain technology and its mechanisms for achieving consensus among decentralized network participants.

Lamport's Byzantine Generals Problem remains a fundamental concept in understanding and addressing the challenges of achieving consensus in distributed computing systems, serving as a cornerstone in the evolution of fault-tolerant and decentralized systems.


Example 1: Every system could experience a byzantine fault problem, different components of an electronic system could be faulty, the ability of the system to continue operating even though it is not at its optimal performance, indicates it has a tolerance. The concept is more defined in a system where decisions are made by democracy ( the choice or option with the most supporters) and not through a central authority.

Example 2: Only decentralized systems face the Byzantine Generals problem, as they have no reliable source of information and no way of verifying the information they receive from other members of the network. In centralized systems, an authority is trusted to publish true information and prevent false or fraudulent information from being spread throughout the network. i.e., if a bank did attempt to lie about a customer's balances or transactions, a central bank is trusted to rectify the breach of trust.

Example 3: Satoshi devised a means to use cryptographic security and public-key encryption to answer the Byzantine general problem in a digital electronic network. To prevent data tampering, cryptographic security uses hashing, a process of encoding. The identity of a network user is verified via public key encryption.

Example 4: Bitcoin managed to solve the Byzantine Generals Problem by using a Proof-of-Work mechanism in order to establish a clear, objective ruleset for the blockchain. In order to add information, called blocks, to the blockchain, a member of the network must publish proof that they invested considerable work into creating the block. This work imposes large costs on the creator, and thus incentivizes them to publish honest information.

  • Byzantine Fault Tolerance (BFT): Byzantine Fault Tolerance (BFT) is an important resilience system that is used in distributed computing systems. It ensures that if a system is attacked, the system will continue to work, despite the attack.

  • Distributed Network: A distributed network in cryptocurrency refers to a decentralized system where the data, transactions, and processing power are spread across many nodes in a network, rather than being concentrated in a central location.